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Found 31 papers, 5 papers with code
Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks
Based on this bound, we minimize a weighted utility function for jointly configuring the controllable parameters to train the RawHFL energy efficiently under practical resource constraints.
A Scalable and Generalizable Pathloss Map Prediction
Large-scale channel prediction, i. e., estimation of the pathloss from geographical/morphological/building maps, is an essential component of wireless network planning.
Handover Protocol Learning for LEO Satellite Networks: Access Delay and Collision Minimization
This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks' HO procedures.
Exploiting Semantic Localization in Highly Dynamic Wireless Networks Using Deep Homoscedastic Domain Adaptation
We therefore consider the blockage by such dynamic objects as another semantic state.
Stochastic Geometry Analysis of a New GSCM with Dual Visibility Regions
For example, the condition of visibility of a scatterer from the perspective of a BS is different from that of an MS and depends on the height of the scatterer.
Novel LoS $β-γ$ THz Channel Unifying Molecular Re-radiation Manifestations
This paper introduces a novel line-of-sight (LoS) $\beta-\gamma$ terahertz (THz) channel model that closely mirrors physical reality by considering radiation trapping.
Simple and Effective Augmentation Methods for CSI Based Indoor Localization
We also showed that if we further augment the dataset with the proposed techniques, test accuracy is improved more than three-fold.
Robust Optimization of RIS in Terahertz under Extreme Molecular Re-radiation Manifestations
Terahertz (THz) communication signals are susceptible to severe degradation because of the molecular interaction with the atmosphere in the form of subsequent absorption and re-radiation.
THz Band Channel Measurements and Statistical Modeling for Urban Microcellular Environments
The THz band (0. 1-10 THz) has attracted considerable attention for next-generation wireless communications, due to the large amount of available bandwidth that may be key to meet the rapidly increasing data rate requirements.
Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling
This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels.
Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization
To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques.
A Geometry-Based Stochastic Model for Truck Communication Channels in Freeway Scenarios
We validate this model by contrasting the root-mean-square delay spread and the angular spreads of departure/arrival derived from the channel model with the outcomes directly derived from the measurements.
Evaluation on Energy Efficiency of UE in UL Cell-Free Massive MIMO System With Power Control Methods
Cell-free massive multiple-input multiple-output (CF mMIMO) systems are expected to provide faster and more robust connections to user equipments (UEs) by cooperation of a massive number of distributed access points, and to be one of the key technologies for beyond 5G (B5G).
THz Band Channel Measurements and Statistical Modeling for Urban D2D Environments
In this paper, we present a first extensive set of channel measurements for urban outdoor environments that are ultra-wideband (1 GHz 3dB bandwidth), and double-directional where both the transmitter and receiver are at the same height.
Uplink Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channels
Therefore, if the "target" spectral efficiency (SE) is met, it is important to optimize the uplink EE when setting the transmit powers of the UEs.
Intelligent Surface Optimization in Terahertz under Two Manifestations of Molecular Re-radiation
The operation of Terahertz (THz) communication can be significantly impacted by the interaction between the transmitted wave and the molecules in the atmosphere.
Using a Drone Sounder to Measure Channels for Cell-Free Massive MIMO Systems
Measurements of the propagation channel form the basis of all realistic system performance evaluations, as foundation of statistical channel models or to verify ray tracing.
Air-to-Ground Directional Channel Sounder With 64-antenna Dual-polarized Cylindrical Array
Unmanned Aerial Vehicles (UAVs), popularly called drones, are an important part of future wireless communications, either as user equipment that needs communication with a ground station, or as base station in a 3D network.
A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals
In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals.
Co-Located vs Distributed vs Semi-Distributed MIMO: Measurement-Based Evaluation
With the growing interest in cell-free massive multiple-input multiple-output (MIMO) systems, the benefits of single-antenna access points (APs) versus multi-antenna APs must be analyzed in order to optimize deployment.
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
Mobile communications have been undergoing a generational change every ten years or so.
Favorable Propagation with User Cluster Sharing
We use these results to examine the detrimental effects of user cluster sharing on FP behavior, and demonstrate the superior performance of the ULA as compared to the UCA and the HURA with equal inter-element spacing.
Hybrid Beamforming Structure for Massive MIMO System: Full-connection v.s. Partial-connection
In this article we compare the performance of two typical hyrbid beamforming structures for multiuser massive MIMO systems, i. e., the full- and partial-connection structures.
Standardization of Propagation Models: 800 MHz to 100 GHz -- A Historical Perspective
This motivated the development of the first standardized propagation model by the European Cooperation in Science and Technology (COST) 207 working group.
Impact of UAV Wobbling on the Air-to-Ground Wireless Channel
This paper studies the impact of unmanned aerial vehicle (UAV) wobbling on the coherence time of the wireless channel between UAVs and a ground user equipment (UE), using a Rician multi-path channel model.
Information Theory Signal Processing Information Theory
Experimental Investigation of Frequency Domain Channel Extrapolation in Massive MIMO Systems for Zero-Feedback FDD
Estimating downlink (DL) channel state information (CSI) in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems generally requires downlink pilots and feedback overheads.
A Machine Learning Solution for Beam Tracking in mmWave Systems
Utilizing millimeter-wave (mmWave) frequencies for wireless communication in \emph{mobile} systems is challenging since it requires continuous tracking of the beam direction.
Supervised ML Solution for Band Assignment in Dual-Band Systems with Omnidirectional and Directional Antennas
This paper considers the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE).
Inferring Remote Channel State Information: Cramér-Rao Lower Bound and Deep Learning Implementation
Channel state information (CSI) is of vital importance in wireless communication systems.
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach
Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches for signal transmissions.
Band Assignment in Dual Band Systems: A Learning-based Approach
In this work, we use a machine learning framework to provide an efficient and practical solution to the band assignment problem.
